To solve the above-mentioned problems,
we proposed rank-constrained SCM estimation [1]}, which is a postprocessing method for ILRMA.
In this method, to effectively model diffuse noise in an overdetermined situation,
a full-rank SCM of diffuse noise is recovered from the estimates obtained by ILRMA.
Since ILRMA can precisely cancel the directional target source [2]},
an accurate rank-\((M\!-\!1)\)  SCM of diffuse noise can be obtained.
Thus, the rank-constrained SCM estimation enables us to restore the lost spatial basis of diffuse noise,
resulting in accurate extraction of the directional target source.
By employing ILRMA as a preprocessing method, we can achieve both lower-cost computation
and more initialization-robust estimation compared with MNMFs.
